Recommender systems are a common component of an e-commerce system. A recommender system functions to select items, such as, for example, consumer products (e.g., books, personal computers, or other consumer goods), entertainment content (e.g., music, movies, TV programs), news stories, web pages, publications, services, and applications, to recommend to a user. Recommender systems may use filtering techniques that attempt to enable the recommender system to select items that are likely to be of interest to the user. Typically, a recommender system that provides personalized recommendations compares a user's profile to some reference characteristics, and seeks to predict a rating that the user would give to an item the user has not yet rated (implicitly or explicitly). These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach).
While recommender systems that function to provide personalized recommendations are prevalent today, there is, nonetheless, a desire for improving such recommender systems.